The stock return synchronicity decreases when the general information environment improves. I theoretically demonstrate that if investors can learn the firm's future performance based on all noisy signals in the market, the systematic volatility would be largely reduced even when the incremental information content of each particular firm's signal is modest. I build up a theoretical model which allows for multiple firms whose cash flows are correlated, and characterize the information as noisy signals about future cash flows. Based on this information structure, the systematic volatility decreases with the resolution of market-level uncertainty when a large amount of public news is released. Since the idiosyncratic volatility would not be affected by the clustered announcements, the stock return synchronicity is predicted to be lower when the information environment becomes better. The earnings season serves as a proper empirical setting to demonstrate how the general information environment would fluctuate the stock return synchronicity in a dynamic manner. Consistent with the information interpretation of R², I find that the dramatically increased intensity of information disclosures could significantly decrease the stock return synchronicity in China. This dynamic pattern is robust after control for the change of fundamentals, the effect of corporate events, the abnormal returns around the earnings announcements and the change of liquidity. More importantly, the driving force of this dynamic pattern is the reduction of the systematic volatility rather than the increment of the idiosyncratic volatility, and this dynamic pattern is more pronounced for older firms. These findings are not special to China. With the sample of 40 countries around the world, I find that the stock return synchronicity and the systematic volatility are lower in the earnings season than they are in the non-earnings seasons in both country-level and firm-level analyses.